Datasets:

bhujith10

/

multi_class_classification_dataset_openai_predictions

like 0

Title: DeepTriangle: A Deep Learning Approach to Loss Reserving, Abstract: We propose a novel approach for loss reserving based on deep neural networks. The approach allows for jointly modeling of paid losses and claims outstanding, and incorporation of heterogenous inputs. We validate the models on loss reserving data across lines of business, and show that they attain or exceed the predictive accuracy of existing stochastic methods. The models require minimal feature engineering and expert input, and can be automated to produce forecasts at a high frequency.

Title: Using High-Rising Cities to Visualize Performance in Real-Time, Abstract: For developers concerned with a performance drop or improvement in their software, a profiler allows a developer to quickly search and identify bottlenecks and leaks that consume much execution time. Non real-time profilers analyze the history of already executed stack traces, while a real-time profiler outputs the results concurrently with the execution of software, so users can know the results instantaneously. However, a real-time profiler risks providing overly large and complex outputs, which is difficult for developers to quickly analyze. In this paper, we visualize the performance data from a real-time profiler. We visualize program execution as a three-dimensional (3D) city, representing the structure of the program as artifacts in a city (i.e., classes and packages expressed as buildings and districts) and their program executions expressed as the fluctuating height of artifacts. Through two case studies and using a prototype of our proposed visualization, we demonstrate how our visualization can easily identify performance issues such as a memory leak and compare performance changes between versions of a program. A demonstration of the interactive features of our prototype is available at this https URL.

Title: Fast Stochastic Variance Reduced Gradient Method with Momentum Acceleration for Machine Learning, Abstract: Recently, research on accelerated stochastic gradient descent methods (e.g., SVRG) has made exciting progress (e.g., linear convergence for strongly convex problems). However, the best-known methods (e.g., Katyusha) requires at least two auxiliary variables and two momentum parameters. In this paper, we propose a fast stochastic variance reduction gradient (FSVRG) method, in which we design a novel update rule with the Nesterov's momentum and incorporate the technique of growing epoch size. FSVRG has only one auxiliary variable and one momentum weight, and thus it is much simpler and has much lower per-iteration complexity. We prove that FSVRG achieves linear convergence for strongly convex problems and the optimal $\mathcal{O}(1/T^2)$ convergence rate for non-strongly convex problems, where $T$ is the number of outer-iterations. We also extend FSVRG to directly solve the problems with non-smooth component functions, such as SVM. Finally, we empirically study the performance of FSVRG for solving various machine learning problems such as logistic regression, ridge regression, Lasso and SVM. Our results show that FSVRG outperforms the state-of-the-art stochastic methods, including Katyusha.

Title: A vertex-weighted-Least-Squares gradient reconstruction, Abstract: Gradient reconstruction is a key process for the spatial accuracy and robustness of finite volume method, especially in industrial aerodynamic applications in which grid quality affects reconstruction methods significantly. A novel gradient reconstruction method for cell-centered finite volume scheme is introduced. This method is composed of two successive steps. First, a vertex-based weighted-least-squares procedure is implemented to calculate vertex gradients, and then the cell-centered gradients are calculated by an arithmetic averaging procedure. By using these two procedures, extended stencils are implemented in the calculations, and the accuracy of gradient reconstruction is improved by the weighting procedure. In the given test cases, the proposed method is showing improvement on both the accuracy and convergence. Furthermore, the method could be extended to the calculation of viscous fluxes.

Title: The reverse mathematics of theorems of Jordan and Lebesgue, Abstract: The Jordan decomposition theorem states that every function $f \colon [0,1] \to \mathbb{R}$ of bounded variation can be written as the difference of two non-decreasing functions. Combining this fact with a result of Lebesgue, every function of bounded variation is differentiable almost everywhere in the sense of Lebesgue measure. We analyze the strength of these theorems in the setting of reverse mathematics. Over $\mathsf{RCA}_0$, a stronger version of Jordan's result where all functions are continuous is equivalent to $\mathsf{ACA}_0$, while the version stated is equivalent to $\mathsf{WKL}_0$. The result that every function on $[0,1]$ of bounded variation is almost everywhere differentiable is equivalent to $\mathsf{WWKL}_0$. To state this equivalence in a meaningful way, we develop a theory of Martin-Löf randomness over $\mathsf{RCA}_0$.

Title: Superdensity Operators for Spacetime Quantum Mechanics, Abstract: We introduce superdensity operators as a tool for analyzing quantum information in spacetime. Superdensity operators encode spacetime correlation functions in an operator framework, and support a natural generalization of Hilbert space techniques and Dirac's transformation theory as traditionally applied to standard density operators. Superdensity operators can be measured experimentally, but accessing their full content requires novel procedures. We demonstrate these statements on several examples. The superdensity formalism suggests useful definitions of spacetime entropies and spacetime quantum channels. For example, we show that the von Neumann entropy of a superdensity operator is related to a quantum generalization of the Kolmogorov-Sinai entropy, and compute this for a many-body system. We also suggest experimental protocols for measuring spacetime entropies.

Title: CUSBoost: Cluster-based Under-sampling with Boosting for Imbalanced Classification, Abstract: Class imbalance classification is a challenging research problem in data mining and machine learning, as most of the real-life datasets are often imbalanced in nature. Existing learning algorithms maximise the classification accuracy by correctly classifying the majority class, but misclassify the minority class. However, the minority class instances are representing the concept with greater interest than the majority class instances in real-life applications. Recently, several techniques based on sampling methods (under-sampling of the majority class and over-sampling the minority class), cost-sensitive learning methods, and ensemble learning have been used in the literature for classifying imbalanced datasets. In this paper, we introduce a new clustering-based under-sampling approach with boosting (AdaBoost) algorithm, called CUSBoost, for effective imbalanced classification. The proposed algorithm provides an alternative to RUSBoost (random under-sampling with AdaBoost) and SMOTEBoost (synthetic minority over-sampling with AdaBoost) algorithms. We evaluated the performance of CUSBoost algorithm with the state-of-the-art methods based on ensemble learning like AdaBoost, RUSBoost, SMOTEBoost on 13 imbalance binary and multi-class datasets with various imbalance ratios. The experimental results show that the CUSBoost is a promising and effective approach for dealing with highly imbalanced datasets.

Title: Finding events in temporal networks: Segmentation meets densest-subgraph discovery, Abstract: In this paper we study the problem of discovering a timeline of events in a temporal network. We model events as dense subgraphs that occur within intervals of network activity. We formulate the event-discovery task as an optimization problem, where we search for a partition of the network timeline into k non-overlapping intervals, such that the intervals span subgraphs with maximum total density. The output is a sequence of dense subgraphs along with corresponding time intervals, capturing the most interesting events during the network lifetime. A naive solution to our optimization problem has polynomial but prohibitively high running time complexity. We adapt existing recent work on dynamic densest-subgraph discovery and approximate dynamic programming to design a fast approximation algorithm. Next, to ensure richer structure, we adjust the problem formulation to encourage coverage of a larger set of nodes. This problem is NP-hard even for static graphs. However, on static graphs a simple greedy algorithm leads to approximate solution due to submodularity. We extended this greedy approach for the case of temporal networks. However, the approximation guarantee does not hold. Nevertheless, according to the experiments, the algorithm finds good quality solutions.

Title: Effects of geometrical frustration on ferromagnetism in the Hubbard model on the Shastry-Sutherland lattice, Abstract: The small-cluster exact-diagonalization calculations and the projector quantum Monte Carlo method are used to examine the competing effects of geometrical frustration and interaction on ferromagnetism in the Hubbard model on the Shastry-Sutherland lattice. It is shown that the geometrical frustration stabilizes the ferromagnetic state at high electron concentrations ($n \gtrsim 7/4$), where strong correlations between ferromagnetism and the shape of the noninteracting density of states are observed. In particular, it is found that ferromagnetism is stabilized only for these values of frustration parameters, which lead to the single peaked noninterating density of states at the band edge. Once, two or more peaks appear in the noninteracting density of states at the band egde the ferromagnetic state is suppressed. This opens a new route towards the understanding of ferromagnetism in strongly correlated systems.

Title: Heteroclinic traveling fronts for a generalized Fisher-Burgers equation with saturating diffusion, Abstract: We study the existence of monotone heteroclinic traveling waves for a general Fisher-Burgers equation with nonlinear and possibly density-dependent diffusion. Such a model arises, for instance, in physical phenomena where a saturation effect appears for large values of the gradient. We give an estimate for the critical speed (namely, the first speed for which a monotone heteroclinic traveling wave exists) for some different shapes of the reaction term, and we analyze its dependence on a small real parameter when this brakes the diffusion, complementing our study with some numerical simulations.

Title: A Capsule based Approach for Polyphonic Sound Event Detection, Abstract: Polyphonic sound event detection (polyphonic SED) is an interesting but challenging task due to the concurrence of multiple sound events. Recently, SED methods based on convolutional neural networks (CNN) and recurrent neural networks (RNN) have shown promising performance. Generally, CNN are designed for local feature extraction while RNN are used to model the temporal dependency among these local features. Despite their success, it is still insufficient for existing deep learning techniques to separate individual sound event from their mixture, largely due to the overlapping characteristic of features. Motivated by the success of Capsule Networks (CapsNet), we propose a more suitable capsule based approach for polyphonic SED. Specifically, several capsule layers are designed to effectively select representative frequency bands for each individual sound event. The temporal dependency of capsule's outputs is then modeled by a RNN. And a dynamic threshold method is proposed for making the final decision based on RNN outputs. Experiments on the TUT-SED Synthetic 2016 dataset show that the proposed approach obtains an F1-score of 68.8% and an error rate of 0.45, outperforming the previous state-of-the-art method of 66.4% and 0.48, respectively.

Title: Variable selection in multivariate linear models with high-dimensional covariance matrix estimation, Abstract: In this paper, we propose a novel variable selection approach in the framework of multivariate linear models taking into account the dependence that may exist between the responses. It consists in estimating beforehand the covariance matrix of the responses and to plug this estimator in a Lasso criterion, in order to obtain a sparse estimator of the coefficient matrix. The properties of our approach are investigated both from a theoretical and a numerical point of view. More precisely, we give general conditions that the estimators of the covariance matrix and its inverse have to satisfy in order to recover the positions of the null and non null entries of the coefficient matrix when the size of the covariance matrix is not fixed and can tend to infinity. We prove that these conditions are satisfied in the particular case of some Toeplitz matrices. Our approach is implemented in the R package MultiVarSel available from the Comprehensive R Archive Network (CRAN) and is very attractive since it benefits from a low computational load. We also assess the performance of our methodology using synthetic data and compare it with alternative approaches. Our numerical experiments show that including the estimation of the covariance matrix in the Lasso criterion dramatically improves the variable selection performance in many cases.

Title: A Novel Model of Cancer-Induced Peripheral Neuropathy and the Role of TRPA1 in Pain Transduction, Abstract: Background. Models of cancer-induced neuropathy are designed by injecting cancer cells near the peripheral nerves. The interference of tissue-resident immune cells does not allow a direct contact with nerve fibres which affects the tumor microenvironment and the invasion process. Methods. Anaplastic tumor-1 (AT-1) cells were inoculated within the sciatic nerves (SNs) of male Copenhagen rats. Lumbar dorsal root ganglia (DRGs) and the SNs were collected on days 3, 7, 14, and 21. SN tissues were examined for morphological changes and DRG tissues for immunofluorescence, electrophoretic tendency, and mRNA quantification. Hypersensitivities to cold, mechanical, and thermal stimuli were determined. HC-030031, a selective TRPA1 antagonist, was used to treat cold allodynia. Results. Nociception thresholds were identified on day 6. Immunofluorescent micrographs showed overexpression of TRPA1 on days 7 and 14 and of CGRP on day 14 until day 21. Both TRPA1 and CGRP were coexpressed on the same cells. Immunoblots exhibited an increase in TRPA1 expression on day 14. TRPA1 mRNA underwent an increase on day 7 (normalized to 18S). Injection of HC-030031 transiently reversed the cold allodynia. Conclusion. A novel and a promising model of cancer-induced neuropathy was established, and the role of TRPA1 and CGRP in pain transduction was examined.

Title: Stability Enhanced Large-Margin Classifier Selection, Abstract: Stability is an important aspect of a classification procedure because unstable predictions can potentially reduce users' trust in a classification system and also harm the reproducibility of scientific conclusions. The major goal of our work is to introduce a novel concept of classification instability, i.e., decision boundary instability (DBI), and incorporate it with the generalization error (GE) as a standard for selecting the most accurate and stable classifier. Specifically, we implement a two-stage algorithm: (i) initially select a subset of classifiers whose estimated GEs are not significantly different from the minimal estimated GE among all the candidate classifiers; (ii) the optimal classifier is chosen as the one achieving the minimal DBI among the subset selected in stage (i). This general selection principle applies to both linear and nonlinear classifiers. Large-margin classifiers are used as a prototypical example to illustrate the above idea. Our selection method is shown to be consistent in the sense that the optimal classifier simultaneously achieves the minimal GE and the minimal DBI. Various simulations and real examples further demonstrate the advantage of our method over several alternative approaches.

Title: Positive Geometries and Canonical Forms, Abstract: Recent years have seen a surprising connection between the physics of scattering amplitudes and a class of mathematical objects--the positive Grassmannian, positive loop Grassmannians, tree and loop Amplituhedra--which have been loosely referred to as "positive geometries". The connection between the geometry and physics is provided by a unique differential form canonically determined by the property of having logarithmic singularities (only) on all the boundaries of the space, with residues on each boundary given by the canonical form on that boundary. In this paper we initiate an exploration of "positive geometries" and "canonical forms" as objects of study in their own right in a more general mathematical setting. We give a precise definition of positive geometries and canonical forms, introduce general methods for finding forms for more complicated positive geometries from simpler ones, and present numerous examples of positive geometries in projective spaces, Grassmannians, and toric, cluster and flag varieties. We also illustrate a number of strategies for computing canonical forms which yield interesting representations for the forms associated with wide classes of positive geometries, ranging from the simplest Amplituhedra to new expressions for the volume of arbitrary convex polytopes.

Title: Spincaloritronic signal generation in non-degenerate Si, Abstract: Spincaloritronic signal generation due to thermal spin injection and spin transport is demonstrated in a non-degenerate Si spin valve. The spin-dependent Seebeck effect is used for the spincaloritronic signal generation, and the thermal gradient of about 200 mK at an interface of Fe and Si enables generating a spin voltage of 8 {\mu}V at room temperature. A simple expansion of a conventional spin drift-diffusion model with taking into account the spin-dependent Seebeck effect shows semiconductor materials are quite potential for the spincaloritronic signal generation comparing with metallic materials, which can allow efficient heat recycling in semiconductor spin devices.

Title: A Markov Chain Theory Approach to Characterizing the Minimax Optimality of Stochastic Gradient Descent (for Least Squares), Abstract: This work provides a simplified proof of the statistical minimax optimality of (iterate averaged) stochastic gradient descent (SGD), for the special case of least squares. This result is obtained by analyzing SGD as a stochastic process and by sharply characterizing the stationary covariance matrix of this process. The finite rate optimality characterization captures the constant factors and addresses model mis-specification.

Title: PULSEDYN - A dynamical simulation tool for studying strongly nonlinear chains, Abstract: We introduce PULSEDYN, a particle dynamics program in $C++$, to solve many-body nonlinear systems in one dimension. PULSEDYN is designed to make computing accessible to non-specialists in the field of nonlinear dynamics of many-body systems and to ensure transparency and easy benchmarking of numerical results for an integrable model (Toda chain) and three non-integrable models (Fermi-Pasta-Ulam-Tsingou, Morse and Lennard-Jones). To achieve the latter, we have made our code open source and free to distribute. We examine (i) soliton propagation and two-soliton collision in the Toda system, (ii) the recurrence phenomenon in the Fermi-Pasta-Ulam-Tsingou system and the decay of a single localized nonlinear excitation in the same system through quasi-equilibrium to an equipartitioned state, and SW propagation in chains with (iii) Morse and (iv) Lennard-Jones potentials. We recover well known results from theory and other numerical results in the literature. We have obtained these results by setting up a parameter file interface which allows the code to be used as a black box. Therefore, we anticipate that the code would prove useful to students and non-specialists. At the same time, PULSEDYN provides scientifically accurate simulations thus making the study of rich dynamical processes broadly accessible.

Title: Stabilization Control of the Differential Mobile Robot Using Lyapunov Function and Extended Kalman Filter, Abstract: This paper presents the design of a control model to navigate the differential mobile robot to reach the desired destination from an arbitrary initial pose. The designed model is divided into two stages: the state estimation and the stabilization control. In the state estimation, an extended Kalman filter is employed to optimally combine the information from the system dynamics and measurements. Two Lyapunov functions are constructed that allow a hybrid feedback control law to execute the robot movements. The asymptotical stability and robustness of the closed loop system are assured. Simulations and experiments are carried out to validate the effectiveness and applicability of the proposed approach.

Title: A family of transformed copulas with singular component, Abstract: In this paper, we present a family of bivariate copulas by transforming a given copula function with two increasing functions, named as transformed copula. One distinctive characteristic of the transformed copula is its singular component along the main diagonal. Conditions guaranteeing the transformed function to be a copula function are provided, and several classes of the transformed copulas are given. The singular component along the main diagonal of the transformed copula is verified, and the tail dependence coefficients of the transformed copulas are obtained. Finally, some properties of the transformed copula are discussed, such as the totally positive of order 2 and the concordance order.

Title: Scalable Importance Tempering and Bayesian Variable Selection, Abstract: We propose a Monte Carlo algorithm to sample from high-dimensional probability distributions that combines Markov chain Monte Carlo (MCMC) and importance sampling. We provide a careful theoretical analysis, including guarantees on robustness to high-dimensionality, explicit comparison with standard MCMC and illustrations of the potential improvements in efficiency. Simple and concrete intuition is provided for when the novel scheme is expected to outperform standard schemes. When applied to Bayesian Variable Selection problems, the novel algorithm is orders of magnitude more efficient than available alternative sampling schemes and allows to perform fast and reliable fully Bayesian inferences with tens of thousands regressors.

Title: Boundary feedback stabilization of a flexible wing model under unsteady aerodynamic loads, Abstract: This paper addresses the boundary stabilization of a flexible wing model, both in bending and twisting displacements, under unsteady aerodynamic loads, and in presence of a store. The wing dynamics is captured by a distributed parameter system as a coupled Euler-Bernoulli and Timoshenko beam model. The problem is tackled in the framework of semigroup theory, and a Lyapunov-based stability analysis is carried out to assess that the system energy, as well as the bending and twisting displacements, decay exponentially to zero. The effectiveness of the proposed boundary control scheme is evaluated based on simulations.

Title: Commuting graphs on Coxeter groups, Dynkin diagrams and finite subgroups of $SL(2,\mathbb{C})$, Abstract: For a group $H$ and a non empty subset $\Gamma\subseteq H$, the commuting graph $G=\mathcal{C}(H,\Gamma)$ is the graph with $\Gamma$ as the node set and where any $x,y \in \Gamma$ are joined by an edge if $x$ and $y$ commute in $H$. We prove that any simple graph can be obtained as a commuting graph of a Coxeter group, solving the realizability problem in this setup. In particular we can recover every Dynkin diagram of ADE type as a commuting graph. Thanks to the relation between the ADE classification and finite subgroups of $\SL(2,\C)$, we are able to rephrase results from the {\em McKay correspondence} in terms of generators of the corresponding Coxeter groups. We finish the paper studying commuting graphs $\mathcal{C}(H,\Gamma)$ for every finite subgroup $H\subset\SL(2,\C)$ for different subsets $\Gamma\subseteq H$, and investigating metric properties of them when $\Gamma=H$.

Title: TrajectoryNet: An Embedded GPS Trajectory Representation for Point-based Classification Using Recurrent Neural Networks, Abstract: Understanding and discovering knowledge from GPS (Global Positioning System) traces of human activities is an essential topic in mobility-based urban computing. We propose TrajectoryNet-a neural network architecture for point-based trajectory classification to infer real world human transportation modes from GPS traces. To overcome the challenge of capturing the underlying latent factors in the low-dimensional and heterogeneous feature space imposed by GPS data, we develop a novel representation that embeds the original feature space into another space that can be understood as a form of basis expansion. We also enrich the feature space via segment-based information and use Maxout activations to improve the predictive power of Recurrent Neural Networks (RNNs). We achieve over 98% classification accuracy when detecting four types of transportation modes, outperforming existing models without additional sensory data or location-based prior knowledge.

Title: Proofs of some Propositions of the semi-Intuitionistic Logic with Strong Negation, Abstract: We offer the proofs that complete our article introducing the propositional calculus called semi-intuitionistic logic with strong negation.

Title: Entanglement in topological systems, Abstract: These lecture notes on entanglement in topological systems are part of the 48th IFF Spring School 2017 on Topological Matter: Topological Insulators, Skyrmions and Majoranas at the Forschungszentrum Juelich, Germany. They cover a short discussion on topologically ordered phases and review the two main tools available for detecting topological order - the entanglement entropy and the entanglement spectrum.

Title: Atomic Convolutional Networks for Predicting Protein-Ligand Binding Affinity, Abstract: Empirical scoring functions based on either molecular force fields or cheminformatics descriptors are widely used, in conjunction with molecular docking, during the early stages of drug discovery to predict potency and binding affinity of a drug-like molecule to a given target. These models require expert-level knowledge of physical chemistry and biology to be encoded as hand-tuned parameters or features rather than allowing the underlying model to select features in a data-driven procedure. Here, we develop a general 3-dimensional spatial convolution operation for learning atomic-level chemical interactions directly from atomic coordinates and demonstrate its application to structure-based bioactivity prediction. The atomic convolutional neural network is trained to predict the experimentally determined binding affinity of a protein-ligand complex by direct calculation of the energy associated with the complex, protein, and ligand given the crystal structure of the binding pose. Non-covalent interactions present in the complex that are absent in the protein-ligand sub-structures are identified and the model learns the interaction strength associated with these features. We test our model by predicting the binding free energy of a subset of protein-ligand complexes found in the PDBBind dataset and compare with state-of-the-art cheminformatics and machine learning-based approaches. We find that all methods achieve experimental accuracy and that atomic convolutional networks either outperform or perform competitively with the cheminformatics based methods. Unlike all previous protein-ligand prediction systems, atomic convolutional networks are end-to-end and fully-differentiable. They represent a new data-driven, physics-based deep learning model paradigm that offers a strong foundation for future improvements in structure-based bioactivity prediction.

Title: Notes on relative normalizations of ruled surfaces in the three-dimensional Euclidean space, Abstract: This paper deals with relative normalizations of skew ruled surfaces in the Euclidean space $\mathbb{E}^{3}$. In section 2 we investigate some new formulae concerning the Pick invariant, the relative curvature, the relative mean curvature and the curvature of the relative metric of a relatively normalized ruled surface $\varPhi$ and in section 3 we introduce some special normalizations of it. All ruled surfaces and their corresponding normalizations that make $\varPhi$ an improper or a proper relative sphere are determined in section 4. In the last section we study ruled surfaces, which are \emph{centrally} normalized, i.e., their relative normals at each point lie on the corresponding central plane. Especially we study various properties of the Tchebychev vector field. We conclude the paper by the study of the central image of $\varPhi$.

Title: Ferroelectric control of the giant Rashba spin orbit coupling in GeTe(111)/InP(111) superlattice, Abstract: GeTe wins the renewed research interest due to its giant bulk Rashba spin orbit coupling (SOC), and becomes the father of a new multifunctional material, i.e., ferroelectric Rashba semiconductor. In the present work, we investigate Rashba SOC at the interface of the ferroelectric semiconductor superlattice GeTe(111)/InP(111) by using the first principles calculation. Contribution of the interface electric field and the ferroelectric field to Rashba SOC is revealed. A large modulation to Rashba SOC and a reversal of the spin polarization is obtained by switching the ferroelectric polarization. Our investigation about GeTe(111)/InP(111) superlattice is of great importance in the application of ferroelectric Rashba semiconductor in the spin field effect transistor.

Title: ForestClaw: A parallel algorithm for patch-based adaptive mesh refinement on a forest of quadtrees, Abstract: We describe a parallel, adaptive, multi-block algorithm for explicit integration of time dependent partial differential equations on two-dimensional Cartesian grids. The grid layout we consider consists of a nested hierarchy of fixed size, non-overlapping, logically Cartesian grids stored as leaves in a quadtree. Dynamic grid refinement and parallel partitioning of the grids is done through the use of the highly scalable quadtree/octree library p4est. Because our concept is multi-block, we are able to easily solve on a variety of geometries including the cubed sphere. In this paper, we pay special attention to providing details of the parallel ghost-filling algorithm needed to ensure that both corner and edge ghost regions around each grid hold valid values. We have implemented this algorithm in the ForestClaw code using single-grid solvers from ClawPack, a software package for solving hyperbolic PDEs using finite volumes methods. We show weak and strong scalability results for scalar advection problems on two-dimensional manifold domains on 1 to 64Ki MPI processes, demonstrating neglible regridding overhead.

Title: Nutritionally recommended food for semi- to strict vegetarian diets based on large-scale nutrient composition data, Abstract: Diet design for vegetarian health is challenging due to the limited food repertoire of vegetarians. This challenge can be partially overcome by quantitative, data-driven approaches that utilise massive nutritional information collected for many different foods. Based on large-scale data of foods' nutrient compositions, the recent concept of nutritional fitness helps quantify a nutrient balance within each food with regard to satisfying daily nutritional requirements. Nutritional fitness offers prioritisation of recommended foods using the foods' occurrence in nutritionally adequate food combinations. Here, we systematically identify nutritionally recommendable foods for semi- to strict vegetarian diets through the computation of nutritional fitness. Along with commonly recommendable foods across different diets, our analysis reveals favourable foods specific to each diet, such as immature lima beans for a vegan diet as an amino acid and choline source, and mushrooms for ovo-lacto vegetarian and vegan diets as a vitamin D source. Furthermore, we find that selenium and other essential micronutrients can be subject to deficiency in plant-based diets, and suggest nutritionally-desirable dietary patterns. We extend our analysis to two hypothetical scenarios of highly personalised, plant-based methionine-restricted diets. Our nutrient-profiling approach may provide a useful guide for designing different types of personalised vegetarian diets.

Title: Does data interpolation contradict statistical optimality?, Abstract: We show that learning methods interpolating the training data can achieve optimal rates for the problems of nonparametric regression and prediction with square loss.

Title: MON: Mission-optimized Overlay Networks, Abstract: Large organizations often have users in multiple sites which are connected over the Internet. Since resources are limited, communication between these sites needs to be carefully orchestrated for the most benefit to the organization. We present a Mission-optimized Overlay Network (MON), a hybrid overlay network architecture for maximizing utility to the organization. We combine an offline and an online system to solve non-concave utility maximization problems. The offline tier, the Predictive Flow Optimizer (PFO), creates plans for routing traffic using a model of network conditions. The online tier, MONtra, is aware of the precise local network conditions and is able to react quickly to problems within the network. Either tier alone is insufficient. The PFO may take too long to react to network changes. MONtra only has local information and cannot optimize non-concave mission utilities. However, by combining the two systems, MON is robust and achieves near-optimal utility under a wide range of network conditions. While best-effort overlay networks are well studied, our work is the first to design overlays that are optimized for mission utility.

Title: Inflationary Primordial Black Holes as All Dark Matter, Abstract: Following a new microlensing constraint on primordial black holes (PBHs) with $\sim10^{20}$--$10^{28}\,\mathrm{g}$~[1], we revisit the idea of PBH as all Dark Matter (DM). We have shown that the updated observational constraints suggest the viable mass function for PBHs as all DM to have a peak at $\simeq 10^{20}\,\mathrm{g}$ with a small width $\sigma \lesssim 0.1$, by imposing observational constraints on an extended mass function in a proper way. We have also provided an inflation model that successfully generates PBHs as all DM fulfilling this requirement.

Title: Computational Tools in Weighted Persistent Homology, Abstract: In this paper, we study further properties and applications of weighted homology and persistent homology. We introduce the Mayer-Vietoris sequence and generalized Bockstein spectral sequence for weighted homology. For applications, we show an algorithm to construct a filtration of weighted simplicial complexes from a weighted network. We also prove a theorem that allows us to calculate the mod $p^2$ weighted persistent homology given some information on the mod $p$ weighted persistent homology.

Title: CryptoDL: Deep Neural Networks over Encrypted Data, Abstract: Machine learning algorithms based on deep neural networks have achieved remarkable results and are being extensively used in different domains. However, the machine learning algorithms requires access to raw data which is often privacy sensitive. To address this issue, we develop new techniques to provide solutions for running deep neural networks over encrypted data. In this paper, we develop new techniques to adopt deep neural networks within the practical limitation of current homomorphic encryption schemes. More specifically, we focus on classification of the well-known convolutional neural networks (CNN). First, we design methods for approximation of the activation functions commonly used in CNNs (i.e. ReLU, Sigmoid, and Tanh) with low degree polynomials which is essential for efficient homomorphic encryption schemes. Then, we train convolutional neural networks with the approximation polynomials instead of original activation functions and analyze the performance of the models. Finally, we implement convolutional neural networks over encrypted data and measure performance of the models. Our experimental results validate the soundness of our approach with several convolutional neural networks with varying number of layers and structures. When applied to the MNIST optical character recognition tasks, our approach achieves 99.52\% accuracy which significantly outperforms the state-of-the-art solutions and is very close to the accuracy of the best non-private version, 99.77\%. Also, it can make close to 164000 predictions per hour. We also applied our approach to CIFAR-10, which is much more complex compared to MNIST, and were able to achieve 91.5\% accuracy with approximation polynomials used as activation functions. These results show that CryptoDL provides efficient, accurate and scalable privacy-preserving predictions.

Title: Data-driven modeling of collaboration networks: A cross-domain analysis, Abstract: We analyze large-scale data sets about collaborations from two different domains: economics, specifically 22.000 R&D alliances between 14.500 firms, and science, specifically 300.000 co-authorship relations between 95.000 scientists. Considering the different domains of the data sets, we address two questions: (a) to what extent do the collaboration networks reconstructed from the data share common structural features, and (b) can their structure be reproduced by the same agent-based model. In our data-driven modeling approach we use aggregated network data to calibrate the probabilities at which agents establish collaborations with either newcomers or established agents. The model is then validated by its ability to reproduce network features not used for calibration, including distributions of degrees, path lengths, local clustering coefficients and sizes of disconnected components. Emphasis is put on comparing domains, but also sub-domains (economic sectors, scientific specializations). Interpreting the link probabilities as strategies for link formation, we find that in R&D collaborations newcomers prefer links with established agents, while in co-authorship relations newcomers prefer links with other newcomers. Our results shed new light on the long-standing question about the role of endogenous and exogenous factors (i.e., different information available to the initiator of a collaboration) in network formation.

Title: Tunnelling in Dante's Inferno, Abstract: We study quantum tunnelling in Dante's Inferno model of large field inflation. Such a tunnelling process, which will terminate inflation, becomes problematic if the tunnelling rate is rapid compared to the Hubble time scale at the time of inflation. Consequently, we constrain the parameter space of Dante's Inferno model by demanding a suppressed tunnelling rate during inflation. The constraints are derived and explicit numerical bounds are provided for representative examples. Our considerations are at the level of an effective field theory; hence, the presented constraints have to hold regardless of any UV completion.

Title: A free energy landscape of the capture of CO2 by frustrated Lewis pairs, Abstract: Frustrated Lewis pairs (FLPs) are known for its ability to capture CO2. Although many FLPs have been reported experimentally and several theoretical studies have been carried out to address the reaction mechanism, the individual roles of Lewis acids and bases of FLP in the capture of CO2 is still unclear. In this study, we employed density functional theory (DFT) based metadynamics simulations to investigate the complete path for the capture of CO2 by tBu3P/B(C6F5)3 pair, and to understand the role of the Lewis acid and base. Interestingly, we have found out that the Lewis acids play more important role than Lewis bases. Specifically, the Lewis acids are crucial for catalytical properties and are responsible for both kinetic and thermodynamics control. The Lewis bases, however, have less impact on the catalytic performance and are mainly responsible for the formation of FLP systems. Based on these findings, we propose a thumb of rule for the future synthesis of FLP-based catalyst for the utilization of CO2.

Title: Synthesizing SystemC Code from Delay Hybrid CSP, Abstract: Delay is omnipresent in modern control systems, which can prompt oscillations and may cause deterioration of control performance, invalidate both stability and safety properties. This implies that safety or stability certificates obtained on idealized, delay-free models of systems prone to delayed coupling may be erratic, and further the incorrectness of the executable code generated from these models. However, automated methods for system verification and code generation that ought to address models of system dynamics reflecting delays have not been paid enough attention yet in the computer science community. In our previous work, on one hand, we investigated the verification of delay dynamical and hybrid systems; on the other hand, we also addressed how to synthesize SystemC code from a verified hybrid system modelled by Hybrid CSP (HCSP) without delay. In this paper, we give a first attempt to synthesize SystemC code from a verified delay hybrid system modelled by Delay HCSP (dHCSP), which is an extension of HCSP by replacing ordinary differential equations (ODEs) with delay differential equations (DDEs). We implement a tool to support the automatic translation from dHCSP to SystemC.

Title: Analysis of Annual Cyclone Frequencies over Bay of Bengal: Effect of 2004 Indian Ocean Tsunami, Abstract: This paper discusses the time series trend and variability of the cyclone frequencies over Bay of Bengal, particularly in order to conclude if there is any significant difference in the pattern visible before and after the disastrous 2004 Indian ocean tsunami based on the observed annual cyclone frequency data obtained by India Meteorological Department over the years 1891-2015. Three different categories of cyclones- depression (<34 knots), cyclonic storm (34-47 knots) and severe cyclonic storm (>47 knots) have been analyzed separately using a non-homogeneous Poisson process approach. The estimated intensity functions of the Poisson processes along with their first two derivatives are discussed and all three categories show decreasing trend of the intensity functions after the tsunami. Using an exact change-point analysis, we show that the drops in mean intensity functions are significant for all three categories. As of author's knowledge, no study so far have discussed the relation between cyclones and tsunamis. Bay of Bengal is surrounded by one of the most densely populated areas of the world and any kind of significant change in tropical cyclone pattern has a large impact in various ways, for example, disaster management planning and our study is immensely important from that perspective.

Title: Pretest and Stein-Type Estimations in Quantile Regression Model, Abstract: In this study, we consider preliminary test and shrinkage estimation strategies for quantile regression models. In classical Least Squares Estimation (LSE) method, the relationship between the explanatory and explained variables in the coordinate plane is estimated with a mean regression line. In order to use LSE, there are three main assumptions on the error terms showing white noise process of the regression model, also known as Gauss-Markov Assumptions, must be met: (1) The error terms have zero mean, (2) The variance of the error terms is constant and (3) The covariance between the errors is zero i.e., there is no autocorrelation. However, data in many areas, including econometrics, survival analysis and ecology, etc. does not provide these assumptions. First introduced by Koenker, quantile regression has been used to complement this deficiency of classical regression analysis and to improve the least square estimation. The aim of this study is to improve the performance of quantile regression estimators by using pre-test and shrinkage strategies. A Monte Carlo simulation study including a comparison with quantile $L_1$--type estimators such as Lasso, Ridge and Elastic Net are designed to evaluate the performances of the estimators. Two real data examples are given for illustrative purposes. Finally, we obtain the asymptotic results of suggested estimators

Title: Exploiting routinely collected severe case data to monitor and predict influenza outbreaks, Abstract: Influenza remains a significant burden on health systems. Effective responses rely on the timely understanding of the magnitude and the evolution of an outbreak. For monitoring purposes, data on severe cases of influenza in England are reported weekly to Public Health England. These data are both readily available and have the potential to provide valuable information to estimate and predict the key transmission features of seasonal and pandemic influenza. We propose an epidemic model that links the underlying unobserved influenza transmission process to data on severe influenza cases. Within a Bayesian framework, we infer retrospectively the parameters of the epidemic model for each seasonal outbreak from 2012 to 2015, including: the effective reproduction number; the initial susceptibility; the probability of admission to intensive care given infection; and the effect of school closure on transmission. The model is also implemented in real time to assess whether early forecasting of the number of admission to intensive care is possible. Our model of admissions data allows reconstruction of the underlying transmission dynamics revealing: increased transmission during the season 2013/14 and a noticeable effect of Christmas school holiday on disease spread during season 2012/13 and 2014/15. When information on the initial immunity of the population is available, forecasts of the number of admissions to intensive care can be substantially improved. Readily available severe case data can be effectively used to estimate epidemiological characteristics and to predict the evolution of an epidemic, crucially allowing real-time monitoring of the transmission and severity of the outbreak.

Title: Progressive Growing of GANs for Improved Quality, Stability, and Variation, Abstract: We describe a new training methodology for generative adversarial networks. The key idea is to grow both the generator and discriminator progressively: starting from a low resolution, we add new layers that model increasingly fine details as training progresses. This both speeds the training up and greatly stabilizes it, allowing us to produce images of unprecedented quality, e.g., CelebA images at 1024^2. We also propose a simple way to increase the variation in generated images, and achieve a record inception score of 8.80 in unsupervised CIFAR10. Additionally, we describe several implementation details that are important for discouraging unhealthy competition between the generator and discriminator. Finally, we suggest a new metric for evaluating GAN results, both in terms of image quality and variation. As an additional contribution, we construct a higher-quality version of the CelebA dataset.

Title: Implicit Media Tagging and Affect Prediction from video of spontaneous facial expressions, recorded with depth camera, Abstract: We present a method that automatically evaluates emotional response from spontaneous facial activity recorded by a depth camera. The automatic evaluation of emotional response, or affect, is a fascinating challenge with many applications, including human-computer interaction, media tagging and human affect prediction. Our approach in addressing this problem is based on the inferred activity of facial muscles over time, as captured by a depth camera recording an individual's facial activity. Our contribution is two-fold: First, we constructed a database of publicly available short video clips, which elicit a strong emotional response in a consistent manner across different individuals. Each video was tagged by its characteristic emotional response along 4 scales: \emph{Valence, Arousal, Likability} and \emph{Rewatch} (the desire to watch again). The second contribution is a two-step prediction method, based on learning, which was trained and tested using this database of tagged video clips. Our method was able to successfully predict the aforementioned 4 dimensional representation of affect, as well as to identify the period of strongest emotional response in the viewing recordings, in a method that is blind to the video clip being watch, revealing a significantly high agreement between the recordings of independent viewers.

Title: Modeling Study of Laser Beam Scattering by Defects on Semiconductor Wafers, Abstract: Accurate modeling of light scattering from nanometer scale defects on Silicon wafers is critical for enabling increasingly shrinking semiconductor technology nodes of the future. Yet, such modeling of defect scattering remains unsolved since existing modeling techniques fail to account for complex defect and wafer geometries. Here, we present results of laser beam scattering from spherical and ellipsoidal particles located on the surface of a silicon wafer. A commercially available electromagnetic field solver (HFSS) was deployed on a multiprocessor cluster to obtain results with previously unknown accuracy down to light scattering intensity of -170 dB. We compute three dimensional scattering patterns of silicon nanospheres located on a semiconductor wafer for both perpendicular and parallel polarization and show the effect of sphere size on scattering. We further computer scattering patterns of nanometer scale ellipsoidal particles having different orientation angles and unveil the effects of ellipsoidal orientation on scattering.

Title: On the Uplink Achievable Rate of Massive MIMO System With Low-Resolution ADC and RF Impairments, Abstract: This paper considers channel estimation and uplink achievable rate of the coarsely quantized massive multiple-input multiple-output (MIMO) system with radio frequency (RF) impairments. We utilize additive quantization noise model (AQNM) and extended error vector magnitude (EEVM) model to analyze the impacts of low-resolution analog-to-digital converters (ADCs) and RF impairments respectively. We show that hardware impairments cause a nonzero floor on the channel estimation error, which contraries to the conventional case with ideal hardware. The maximal-ratio combining (MRC) technique is then used at the receiver, and an approximate tractable expression for the uplink achievable rate is derived. The simulation results illustrate the appreciable compensations between ADCs' resolution and RF impairments. The proposed studies support the feasibility of equipping economical coarse ADCs and economical imperfect RF components in practical massive MIMO systems.

Title: Arithmetic statistics of modular symbols, Abstract: Mazur, Rubin, and Stein have recently formulated a series of conjectures about statistical properties of modular symbols in order to understand central values of twists of elliptic curve $L$-functions. Two of these conjectures relate to the asymptotic growth of the first and second moments of the modular symbols. We prove these on average by using analytic properties of Eisenstein series twisted by modular symbols. Another of their conjectures predicts the Gaussian distribution of normalized modular symbols ordered according to the size of the denominator of the cusps. We prove this conjecture in a refined version that also allows restrictions on the location of the cusps.

Title: Quantifying the uncertainties in an ensemble of decadal climate predictions, Abstract: Meaningful climate predictions must be accompanied by their corresponding range of uncertainty. Quantifying the uncertainties is non-trivial, and different methods have been suggested and used in the past. Here, we propose a method that does not rely on any assumptions regarding the distribution of the ensemble member predictions. The method is tested using the CMIP5 1981-2010 decadal predictions and is shown to perform better than two other methods considered here. The improved estimate of the uncertainties is of great importance for both practical use and for better assessing the significance of the effects seen in theoretical studies.

Title: A Learning-Based Framework for Two-Dimensional Vehicle Maneuver Prediction over V2V Networks, Abstract: Situational awareness in vehicular networks could be substantially improved utilizing reliable trajectory prediction methods. More precise situational awareness, in turn, results in notably better performance of critical safety applications, such as Forward Collision Warning (FCW), as well as comfort applications like Cooperative Adaptive Cruise Control (CACC). Therefore, vehicle trajectory prediction problem needs to be deeply investigated in order to come up with an end to end framework with enough precision required by the safety applications' controllers. This problem has been tackled in the literature using different methods. However, machine learning, which is a promising and emerging field with remarkable potential for time series prediction, has not been explored enough for this purpose. In this paper, a two-layer neural network-based system is developed which predicts the future values of vehicle parameters, such as velocity, acceleration, and yaw rate, in the first layer and then predicts the two-dimensional, i.e. longitudinal and lateral, trajectory points based on the first layer's outputs. The performance of the proposed framework has been evaluated in realistic cut-in scenarios from Safety Pilot Model Deployment (SPMD) dataset and the results show a noticeable improvement in the prediction accuracy in comparison with the kinematics model which is the dominant employed model by the automotive industry. Both ideal and nonideal communication circumstances have been investigated for our system evaluation. For non-ideal case, an estimation step is included in the framework before the parameter prediction block to handle the drawbacks of packet drops or sensor failures and reconstruct the time series of vehicle parameters at a desirable frequency.

Title: A contract-based method to specify stimulus-response requirements, Abstract: A number of formal methods exist for capturing stimulus-response requirements in a declarative form. Someone yet needs to translate the resulting declarative statements into imperative programs. The present article describes a method for specification and verification of stimulus-response requirements in the form of imperative program routines with conditionals and assertions. A program prover then checks a candidate program directly against the stated requirements. The article illustrates the approach by applying it to an ASM model of the Landing Gear System, a widely used realistic example proposed for evaluating specification and verification techniques.

Title: On the Diophantine equation $\sum_{j=1}^kjF_j^p=F_n^q$, Abstract: Let $F_n$ denote the $n^{th}$ term of the Fibonacci sequence. In this paper, we investigate the Diophantine equation $F_1^p+2F_2^p+\cdots+kF_{k}^p=F_{n}^q$ in the positive integers $k$ and $n$, where $p$ and $q$ are given positive integers. A complete solution is given if the exponents are included in the set $\{1,2\}$. Based on the specific cases we could solve, and a computer search with $p,q,k\le100$ we conjecture that beside the trivial solutions only $F_8=F_1+2F_2+3F_3+4F_4$, $F_4^2=F_1+2F_2+3F_3$, and $F_4^3=F_1^3+2F_2^3+3F_3^3$ satisfy the title equation.

Title: Information Theory of Data Privacy, Abstract: By combining Shannon's cryptography model with an assumption to the lower bound of adversaries' uncertainty to the queried dataset, we develop a secure Bayesian inference-based privacy model and then in some extent answer Dwork et al.'s question [1]: "why Bayesian risk factors are the right measure for privacy loss". This model ensures an adversary can only obtain little information of each individual from the model's output if the adversary's uncertainty to the queried dataset is larger than the lower bound. Importantly, the assumption to the lower bound almost always holds, especially for big datasets. Furthermore, this model is flexible enough to balance privacy and utility: by using four parameters to characterize the assumption, there are many approaches to balance privacy and utility and to discuss the group privacy and the composition privacy properties of this model.

Title: A combined entropy and utility based generative model for large scale multiple discrete-continuous travel behaviour data, Abstract: Generative models, either by simple clustering algorithms or deep neural network architecture, have been developed as a probabilistic estimation method for dimension reduction or to model the underlying properties of data structures. Although their apparent use has largely been limited to image recognition and classification, generative machine learning algorithms can be a powerful tool for travel behaviour research. In this paper, we examine the generative machine learning approach for analyzing multiple discrete-continuous (MDC) travel behaviour data to understand the underlying heterogeneity and correlation, increasing the representational power of such travel behaviour models. We show that generative models are conceptually similar to choice selection behaviour process through information entropy and variational Bayesian inference. Specifically, we consider a restricted Boltzmann machine (RBM) based algorithm with multiple discrete-continuous layer, formulated as a variational Bayesian inference optimization problem. We systematically describe the proposed machine learning algorithm and develop a process of analyzing travel behaviour data from a generative learning perspective. We show parameter stability from model analysis and simulation tests on an open dataset with multiple discrete-continuous dimensions and a size of 293,330 observations. For interpretability, we derive analytical methods for conditional probabilities as well as elasticities. Our results indicate that latent variables in generative models can accurately represent joint distribution consistently w.r.t multiple discrete-continuous variables. Lastly, we show that our model can generate statistically similar data distributions for travel forecasting and prediction.

Title: Direct Mapping Hidden Excited State Interaction Patterns from ab initio Dynamics and Its Implications on Force Field Development, Abstract: The excited states of polyatomic systems are rather complex, and often exhibit meta-stable dynamical behaviors. Static analysis of reaction pathway often fails to sufficiently characterize excited state motions due to their highly non-equilibrium nature. Here, we proposed a time series guided clustering algorithm to generate most relevant meta-stable patterns directly from ab initio dynamic trajectories. Based on the knowledge of these meta-stable patterns, we suggested an interpolation scheme with only a concrete and finite set of known patterns to accurately predict the ground and excited state properties of the entire dynamics trajectories. As illustrated with the example of sinapic acids, the estimation error for both ground and excited state is very close, which indicates one could predict the ground and excited state molecular properties with similar accuracy. These results may provide us some insights to construct an excited state force field with compatible energy terms as traditional ones.

Title: Discovery of Intrinsic Quantum Anomalous Hall Effect in Organic Mn-DCA Lattice, Abstract: The quantum anomalous Hall (QAH) phase is a novel topological state of matter characterized by a nonzero quantized Hall conductivity without an external magnetic field. The realizations of QAH effect, however, are experimentally challengeable. Based on ab initio calculations, here we propose an intrinsic QAH phase in DCA Kagome lattice. The nontrivial topology in Kagome bands are confirmed by the nonzero chern number, quantized Hall conductivity, and gapless chiral edge states of Mn-DCA lattice. A tight-binding (TB) model is further constructed to clarify the origin of QAH effect. Furthermore, its Curie temperature, estimated to be ~ 253 K using Monte-Carlo simulation, is comparable with room temperature and higher than most of two-dimensional ferromagnetic thin films. Our findings present a reliable material platform for the observation of QAH effect in covalent-organic frameworks.

Title: On the application of Laguerre's method to the polynomial eigenvalue problem, Abstract: The polynomial eigenvalue problem arises in many applications and has received a great deal of attention over the last decade. The use of root-finding methods to solve the polynomial eigenvalue problem dates back to the work of Kublanovskaya (1969, 1970) and has received a resurgence due to the work of Bini and Noferini (2013). In this paper, we present a method which uses Laguerre iteration for computing the eigenvalues of a matrix polynomial. An effective method based on the numerical range is presented for computing initial estimates to the eigenvalues of a matrix polynomial. A detailed explanation of the stopping criteria is given, and it is shown that under suitable conditions we can guarantee the backward stability of the eigenvalues computed by our method. Then, robust methods are provided for computing both the right and left eigenvectors and the condition number of each eigenpair. Applications for Hessenberg and tridiagonal matrix polynomials are given and we show that both structures benefit from substantial computational savings. Finally, we present several numerical experiments to verify the accuracy of our method and its competitiveness for solving the roots of a polynomial and the tridiagonal eigenvalue problem.

Title: Invariant measures for the actions of the modular group, Abstract: In this note, we give a nature action of the modular group on the ends of the infinite (p + 1)-cayley tree, for each prime p. We show that there is a unique invariant probability measure for each p.

Title: Winds and radiation in unison: a new semi-analytic feedback model for cloud dissolution, Abstract: Star clusters interact with the interstellar medium (ISM) in various ways, most importantly in the destruction of molecular star-forming clouds, resulting in inefficient star formation on galactic scales. On cloud scales, ionizing radiation creates \hii regions, while stellar winds and supernovae drive the ISM into thin shells. These shells are accelerated by the combined effect of winds, radiation pressure and supernova explosions, and slowed down by gravity. Since radiative and mechanical feedback is highly interconnected, they must be taken into account in a self-consistent and combined manner, including the coupling of radiation and matter. We present a new semi-analytic one-dimensional feedback model for isolated massive clouds ($\geq 10^5\,M_{\odot}$) to calculate shell dynamics and shell structure simultaneously. It allows us to scan a large range of physical parameters (gas density, star formation efficiency, metallicity) and to estimate escape fractions of ionizing radiation $f_{\rm{esc,i}}$, the minimum star formation efficiency $\epsilon_{\rm{min}}$ required to drive an outflow, and recollapse time scales for clouds that are not destroyed by feedback. Our results show that there is no simple answer to the question of what dominates cloud dynamics, and that each feedback process significantly influences the efficiency of the others. We find that variations in natal cloud density can very easily explain differences between dense-bound and diffuse-open star clusters. We also predict, as a consequence of feedback, a $4-6$ Myr age difference for massive clusters with multiple generations.

Title: Analysis-of-marginal-Tail-Means (ATM): a robust method for discrete black-box optimization, Abstract: We present a new method, called Analysis-of-marginal-Tail-Means (ATM), for effective robust optimization of discrete black-box problems. ATM has important applications to many real-world engineering problems (e.g., manufacturing optimization, product design, molecular engineering), where the objective to optimize is black-box and expensive, and the design space is inherently discrete. One weakness of existing methods is that they are not robust: these methods perform well under certain assumptions, but yield poor results when such assumptions (which are difficult to verify in black-box problems) are violated. ATM addresses this via the use of marginal tail means for optimization, which combines both rank-based and model-based methods. The trade-off between rank- and model-based optimization is tuned by first identifying important main effects and interactions, then finding a good compromise which best exploits additive structure. By adaptively tuning this trade-off from data, ATM provides improved robust optimization over existing methods, particularly in problems with (i) a large number of factors, (ii) unordered factors, or (iii) experimental noise. We demonstrate the effectiveness of ATM in simulations and in two real-world engineering problems: the first on robust parameter design of a circular piston, and the second on product family design of a thermistor network.

Title: Learning Instance Segmentation by Interaction, Abstract: We present an approach for building an active agent that learns to segment its visual observations into individual objects by interacting with its environment in a completely self-supervised manner. The agent uses its current segmentation model to infer pixels that constitute objects and refines the segmentation model by interacting with these pixels. The model learned from over 50K interactions generalizes to novel objects and backgrounds. To deal with noisy training signal for segmenting objects obtained by self-supervised interactions, we propose robust set loss. A dataset of robot's interactions along-with a few human labeled examples is provided as a benchmark for future research. We test the utility of the learned segmentation model by providing results on a downstream vision-based control task of rearranging multiple objects into target configurations from visual inputs alone. Videos, code, and robotic interaction dataset are available at this https URL

Title: Generating Representative Executions [Extended Abstract], Abstract: Analyzing the behaviour of a concurrent program is made difficult by the number of possible executions. This problem can be alleviated by applying the theory of Mazurkiewicz traces to focus only on the canonical representatives of the equivalence classes of the possible executions of the program. This paper presents a generic framework that allows to specify the possible behaviours of the execution environment, and generate all Foata-normal executions of a program, for that environment, by discarding abnormal executions during the generation phase. The key ingredient of Mazurkiewicz trace theory, the dependency relation, is used in the framework in two roles: first, as part of the specification of which executions are allowed at all, and then as part of the normality checking algorithm, which is used to discard the abnormal executions. The framework is instantiated to the relaxed memory models of the SPARC hierarchy.

Title: Outlier Detection by Consistent Data Selection Method, Abstract: Often the challenge associated with tasks like fraud and spam detection[1] is the lack of all likely patterns needed to train suitable supervised learning models. In order to overcome this limitation, such tasks are attempted as outlier or anomaly detection tasks. We also hypothesize that out- liers have behavioral patterns that change over time. Limited data and continuously changing patterns makes learning significantly difficult. In this work we are proposing an approach that detects outliers in large data sets by relying on data points that are consistent. The primary contribution of this work is that it will quickly help retrieve samples for both consistent and non-outlier data sets and is also mindful of new outlier patterns. No prior knowledge of each set is required to extract the samples. The method consists of two phases, in the first phase, consistent data points (non- outliers) are retrieved by an ensemble method of unsupervised clustering techniques and in the second phase a one class classifier trained on the consistent data point set is ap- plied on the remaining sample set to identify the outliers. The approach is tested on three publicly available data sets and the performance scores are competitive.

Title: Evidence for Two Hot Jupiter Formation Paths, Abstract: Disk migration and high-eccentricity migration are two well-studied theories to explain the formation of hot Jupiters. The former predicts that these planets can migrate up until the planet-star Roche separation ($a_{Roche}$) and the latter predicts they will tidally circularize at a minimum distance of 2$a_{Roche}$. Considering long-running radial velocity and transit surveys have identified a couple hundred hot Jupiters to date, we can revisit the classic question of hot Jupiter formation in a data-driven manner. We approach this problem using data from several exoplanet surveys (radial velocity, Kepler, HAT, and WASP) allowing for either a single population or a mixture of populations associated with these formation channels, and applying a hierarchical Bayesian mixture model of truncated power laws of the form $x^{\gamma-1}$ to constrain the population-level parameters of interest (e.g., location of inner edges, $\gamma$, mixture fractions). Within the limitations of our chosen models, we find the current radial velocity and Kepler sample of hot Jupiters can be well explained with a single truncated power law distribution with a lower cutoff near 2$a_{Roche}$, a result that still holds after a decade, and $\gamma=-0.51\pm^{0.19}_{0.20}$. However, the HAT and WASP data show evidence for multiple populations (Bayes factor $\approx 10^{21}$). We find that $15\pm^{9}_{6}\%$ reside in a component consistent with disk migration ($\gamma=-0.04\pm^{0.53}_{1.27}$) and $85\pm^{6}_{9}\%$ in one consistent with high-eccentricity migration ($\gamma=-1.38\pm^{0.32}_{0.47}$). We find no immediately strong connections with some observed host star properties and speculate on how future exoplanet surveys could improve upon hot Jupiter population inference.

Title: Extensile actomyosin?, Abstract: Living cells move thanks to assemblies of actin filaments and myosin motors that range from very organized striated muscle tissue to disordered intracellular bundles. The mechanisms powering these disordered structures are debated, and all models studied so far predict that they are contractile. We reexamine this prediction through a theoretical treatment of the interplay of three well-characterized internal dynamical processes in actomyosin bundles: actin treadmilling, the attachement-detachment dynamics of myosin and that of crosslinking proteins. We show that these processes enable an extensive control of the bundle's active mechanics, including reversals of the filaments' apparent velocities and the possibility of generating extension instead of contraction. These effects offer a new perspective on well-studied in vivo systems, as well as a robust criterion to experimentally elucidate the underpinnings of actomyosin activity.

Title: Direct Multitype Cardiac Indices Estimation via Joint Representation and Regression Learning, Abstract: Cardiac indices estimation is of great importance during identification and diagnosis of cardiac disease in clinical routine. However, estimation of multitype cardiac indices with consistently reliable and high accuracy is still a great challenge due to the high variability of cardiac structures and complexity of temporal dynamics in cardiac MR sequences. While efforts have been devoted into cardiac volumes estimation through feature engineering followed by a independent regression model, these methods suffer from the vulnerable feature representation and incompatible regression model. In this paper, we propose a semi-automated method for multitype cardiac indices estimation. After manual labelling of two landmarks for ROI cropping, an integrated deep neural network Indices-Net is designed to jointly learn the representation and regression models. It comprises two tightly-coupled networks: a deep convolution autoencoder (DCAE) for cardiac image representation, and a multiple output convolution neural network (CNN) for indices regression. Joint learning of the two networks effectively enhances the expressiveness of image representation with respect to cardiac indices, and the compatibility between image representation and indices regression, thus leading to accurate and reliable estimations for all the cardiac indices. When applied with five-fold cross validation on MR images of 145 subjects, Indices-Net achieves consistently low estimation error for LV wall thicknesses (1.44$\pm$0.71mm) and areas of cavity and myocardium (204$\pm$133mm$^2$). It outperforms, with significant error reductions, segmentation method (55.1% and 17.4%) and two-phase direct volume-only methods (12.7% and 14.6%) for wall thicknesses and areas, respectively. These advantages endow the proposed method a great potential in clinical cardiac function assessment.

Title: Electronic structure of transferred graphene/h-BN van der Waals heterostructures with nonzero stacking angles by nano-ARPES, Abstract: In van der Waals heterostructures, the periodic potential from the Moiré superlattice can be used as a control knob to modulate the electronic structure of the constituent materials. Here we present a nanoscale angle-resolved photoemission spectroscopy (Nano-ARPES) study of transferred graphene/h-BN heterostructures with two different stacking angles of 2.4° and 4.3° respectively. Our measurements reveal six replicas of graphene Dirac cones at the superlattice Brillouin zone (SBZ) centers. The size of the SBZ and its relative rotation angle to the graphene BZ are in good agreement with Moiré superlattice period extracted from atomic force microscopy (AFM) measurements. Comparison to epitaxial graphene/h-BN with 0° stacking angles suggests that the interaction between graphene and h-BN decreases with increasing stacking angle.

Title: Robust Regulation of Infinite-Dimensional Port-Hamiltonian Systems, Abstract: We will give general sufficient conditions under which a controller achieves robust regulation for a boundary control and observation system. Utilizing these conditions we construct a minimal order robust controller for an arbitrary order impedance passive linear port-Hamiltonian system. The theoretical results are illustrated with a numerical example where we implement a controller for a one-dimensional Euler-Bernoulli beam with boundary controls and boundary observations.

Title: Extracting urban impervious surface from GF-1 imagery using one-class classifiers, Abstract: Impervious surface area is a direct consequence of the urbanization, which also plays an important role in urban planning and environmental management. With the rapidly technical development of remote sensing, monitoring urban impervious surface via high spatial resolution (HSR) images has attracted unprecedented attention recently. Traditional multi-classes models are inefficient for impervious surface extraction because it requires labeling all needed and unneeded classes that occur in the image exhaustively. Therefore, we need to find a reliable one-class model to classify one specific land cover type without labeling other classes. In this study, we investigate several one-class classifiers, such as Presence and Background Learning (PBL), Positive Unlabeled Learning (PUL), OCSVM, BSVM and MAXENT, to extract urban impervious surface area using high spatial resolution imagery of GF-1, China's new generation of high spatial remote sensing satellite, and evaluate the classification accuracy based on artificial interpretation results. Compared to traditional multi-classes classifiers (ANN and SVM), the experimental results indicate that PBL and PUL provide higher classification accuracy, which is similar to the accuracy provided by ANN model. Meanwhile, PBL and PUL outperforms OCSVM, BSVM, MAXENT and SVM models. Hence, the one-class classifiers only need a small set of specific samples to train models without losing predictive accuracy, which is supposed to gain more attention on urban impervious surface extraction or other one specific land cover type.

Title: Robust, Deep and Inductive Anomaly Detection, Abstract: PCA is a classical statistical technique whose simplicity and maturity has seen it find widespread use as an anomaly detection technique. However, it is limited in this regard by being sensitive to gross perturbations of the input, and by seeking a linear subspace that captures normal behaviour. The first issue has been dealt with by robust PCA, a variant of PCA that explicitly allows for some data points to be arbitrarily corrupted, however, this does not resolve the second issue, and indeed introduces the new issue that one can no longer inductively find anomalies on a test set. This paper addresses both issues in a single model, the robust autoencoder. This method learns a nonlinear subspace that captures the majority of data points, while allowing for some data to have arbitrary corruption. The model is simple to train and leverages recent advances in the optimisation of deep neural networks. Experiments on a range of real-world datasets highlight the model's effectiveness.

Title: Uniqueness and stability of Ricci flow through singularities, Abstract: We verify a conjecture of Perelman, which states that there exists a canonical Ricci flow through singularities starting from an arbitrary compact Riemannian 3-manifold. Our main result is a uniqueness theorem for such flows, which, together with an earlier existence theorem of Lott and the second named author, implies Perelman's conjecture. We also show that this flow through singularities depends continuously on its initial condition and that it may be obtained as a limit of Ricci flows with surgery. Our results have applications to the study of diffeomorphism groups of three manifolds --- in particular to the Generalized Smale Conjecture --- which will appear in a subsequent paper.

Title: A Markov decision process approach to optimizing cancer therapy using multiple modalities, Abstract: There are several different modalities, e.g., surgery, chemotherapy, and radiotherapy, that are currently used to treat cancer. It is common practice to use a combination of these modalities to maximize clinical outcomes, which are often measured by a balance between maximizing tumor damage and minimizing normal tissue side effects due to treatment. However, multi-modality treatment policies are mostly empirical in current practice, and are therefore subject to individual clinicians' experiences and intuition. We present a novel formulation of optimal multi-modality cancer management using a finite-horizon Markov decision process approach. Specifically, at each decision epoch, the clinician chooses an optimal treatment modality based on the patient's observed state, which we define as a combination of tumor progression and normal tissue side effect. Treatment modalities are categorized as (1) Type 1, which has a high risk and high reward, but is restricted in the frequency of administration during a treatment course, (2) Type 2, which has a lower risk and lower reward than Type 1, but may be repeated without restriction, and (3) Type 3, no treatment (surveillance), which has the possibility of reducing normal tissue side effect at the risk of worsening tumor progression. Numerical simulations using various intuitive, concave reward functions show the structural insights of optimal policies and demonstrate the potential applications of using a rigorous approach to optimizing multi-modality cancer management.

Title: Complex Contagions with Timers, Abstract: A great deal of effort has gone into trying to model social influence --- including the spread of behavior, norms, and ideas --- on networks. Most models of social influence tend to assume that individuals react to changes in the states of their neighbors without any time delay, but this is often not true in social contexts, where (for various reasons) different agents can have different response times. To examine such situations, we introduce the idea of a timer into threshold models of social influence. The presence of timers on nodes delays the adoption --- i.e., change of state --- of each agent, which in turn delays the adoptions of its neighbors. With a homogeneous-distributed timer, in which all nodes exhibit the same amount of delay, adoption delays are also homogeneous, so the adoption order of nodes remains the same. However, heterogeneously-distributed timers can change the adoption order of nodes and hence the "adoption paths" through which state changes spread in a network. Using a threshold model of social contagions, we illustrate that heterogeneous timers can either accelerate or decelerate the spread of adoptions compared to an analogous situation with homogeneous timers, and we investigate the relationship of such acceleration or deceleration with respect to timer distribution and network structure. We derive an analytical approximation for the temporal evolution of the fraction of adopters by modifying a pair approximation of the Watts threshold model, and we find good agreement with numerical computations. We also examine our new timer model on networks constructed from empirical data.

Title: Random Networks, Graphical Models, and Exchangeability, Abstract: We study conditional independence relationships for random networks and their interplay with exchangeability. We show that, for finitely exchangeable network models, the empirical subgraph densities are maximum likelihood estimates of their theoretical counterparts. We then characterize all possible Markov structures for finitely exchangeable random graphs, thereby identifying a new class of Markov network models corresponding to bidirected Kneser graphs. In particular, we demonstrate that the fundamental property of dissociatedness corresponds to a Markov property for exchangeable networks described by bidirected line graphs. Finally we study those exchangeable models that are also summarized in the sense that the probability of a network only depends onthe degree distribution, and identify a class of models that is dual to the Markov graphs of Frank and Strauss (1986). Particular emphasis is placed on studying consistency properties of network models under the process of forming subnetworks and we show that the only consistent systems of Markov properties correspond to the empty graph, the bidirected line graph of the complete graph, and the complete graph.

Title: Catching Zika Fever: Application of Crowdsourcing and Machine Learning for Tracking Health Misinformation on Twitter, Abstract: In February 2016, World Health Organization declared the Zika outbreak a Public Health Emergency of International Concern. With developing evidence it can cause birth defects, and the Summer Olympics coming up in the worst affected country, Brazil, the virus caught fire on social media. In this work, use Zika as a case study in building a tool for tracking the misinformation around health concerns on Twitter. We collect more than 13 million tweets -- spanning the initial reports in February 2016 and the Summer Olympics -- regarding the Zika outbreak and track rumors outlined by the World Health Organization and Snopes fact checking website. The tool pipeline, which incorporates health professionals, crowdsourcing, and machine learning, allows us to capture health-related rumors around the world, as well as clarification campaigns by reputable health organizations. In the case of Zika, we discover an extremely bursty behavior of rumor-related topics, and show that, once the questionable topic is detected, it is possible to identify rumor-bearing tweets using automated techniques. Thus, we illustrate insights the proposed tools provide into potentially harmful information on social media, allowing public health researchers and practitioners to respond with a targeted and timely action.

Title: Monte Carlo determination of the low-energy constants for a two-dimensional spin-1 Heisenberg model with spatial anisotropy, Abstract: The low-energy constants, namely the spin stiffness $\rho_s$, the staggered magnetization density ${\cal M}_s$ per area, and the spinwave velocity $c$ of the two-dimensional (2D) spin-1 Heisenberg model on the square and rectangular lattices are determined using the first principles Monte Carlo method. In particular, the studied models have antiferromagnetic couplings $J_1$ and $J_2$ in the spatial 1- and 2-directions, respectively. For each considered $J_2/J_1$, the aspect ratio of the corresponding linear box sizes $L_2/L_1$ used in the simulations is adjusted so that the squares of the two spatial winding numbers take the same values. In addition, the relevant finite-volume and -temperature predictions from magnon chiral perturbation theory are employed in extracting the numerical values of these low-energy constants. Our results of $\rho_{s1}$ are in quantitative agreement with those obtained by the series expansion method over a broad range of $J_2/J_1$. This in turn provides convincing numerical evidence for the quantitative correctness of our approach. The ${\cal M}_s$ and $c$ presented here for the spatially anisotropic models are new and can be used as benchmarks for future related studies.

Title: Accelerating Science with Generative Adversarial Networks: An Application to 3D Particle Showers in Multi-Layer Calorimeters, Abstract: Physicists at the Large Hadron Collider (LHC) rely on detailed simulations of particle collisions to build expectations of what experimental data may look like under different theory modeling assumptions. Petabytes of simulated data are needed to develop analysis techniques, though they are expensive to generate using existing algorithms and computing resources. The modeling of detectors and the precise description of particle cascades as they interact with the material in the calorimeter are the most computationally demanding steps in the simulation pipeline. We therefore introduce a deep neural network-based generative model to enable high-fidelity, fast, electromagnetic calorimeter simulation. There are still challenges for achieving precision across the entire phase space, but our current solution can reproduce a variety of particle shower properties while achieving speed-up factors of up to 100,000$\times$. This opens the door to a new era of fast simulation that could save significant computing time and disk space, while extending the reach of physics searches and precision measurements at the LHC and beyond.

Title: Symmetric calorons and the rotation map, Abstract: We study $SU(2)$ calorons, also known as periodic instantons, and consider invariance under isometries of $S^1\times\mathbb{R}^3$ coupled with a non-spatial isometry called the rotation map. In particular, we investigate the fixed points under various cyclic symmetry groups. Our approach utilises a construction akin to the ADHM construction of instantons -- what we call the monad matrix data for calorons -- derived from the work of Charbonneau and Hurtubise. To conclude, we present an example of how investigating these symmetry groups can help to construct new calorons by deriving Nahm data in the case of charge $2$.

Title: FeaStNet: Feature-Steered Graph Convolutions for 3D Shape Analysis, Abstract: Convolutional neural networks (CNNs) have massively impacted visual recognition in 2D images, and are now ubiquitous in state-of-the-art approaches. CNNs do not easily extend, however, to data that are not represented by regular grids, such as 3D shape meshes or other graph-structured data, to which traditional local convolution operators do not directly apply. To address this problem, we propose a novel graph-convolution operator to establish correspondences between filter weights and graph neighborhoods with arbitrary connectivity. The key novelty of our approach is that these correspondences are dynamically computed from features learned by the network, rather than relying on predefined static coordinates over the graph as in previous work. We obtain excellent experimental results that significantly improve over previous state-of-the-art shape correspondence results. This shows that our approach can learn effective shape representations from raw input coordinates, without relying on shape descriptors.

Title: The best fit for the observed galaxy Counts-in-Cell distribution function, Abstract: The Sloan Digital Sky Survey (SDSS) is the first dense redshift survey encompassing a volume large enough to find the best analytic probability density function that fits the galaxy Counts-in-Cells distribution $f_V(N)$, the frequency distribution of galaxy counts in a volume $V$. Different analytic functions have been previously proposed that can account for some of the observed features of the observed frequency counts, but fail to provide an overall good fit to this important statistical descriptor of the galaxy large-scale distribution. Our goal is to find the probability density function that better fits the observed Counts-in-Cells distribution $f_V(N)$. We have made a systematic study of this function applied to several samples drawn from the SDSS. We show the effective ways to deal with incompleteness of the sample (masked data) in the calculation of $f_V(N)$. We use LasDamas simulations to estimate the errors in the calculation. We test four different distribution functions to find the best fit: the Gravitational Quasi-Equilibrium distribution, the Negative Binomial Distribution, the Log Normal distribution and the Log Normal Distribution including a bias parameter. In the two latter cases, we apply a shot-noise correction to the distributions assuming the local Poisson model. We show that the best fit for the Counts-in-Cells distribution function is provided by the Negative Binomial distribution. In addition, at large scales the Log Normal distribution modified with the inclusion of the bias term also performs a satisfactory fit of the empirical values of $f_V(N)$. Our results demonstrate that the inclusion of a bias term in the Log Normal distribution is necessary to fit the observed galaxy Count-in-Cells distribution function.

Title: On Improving Deep Reinforcement Learning for POMDPs, Abstract: Deep Reinforcement Learning (RL) recently emerged as one of the most competitive approaches for learning in sequential decision making problems with fully observable environments, e.g., computer Go. However, very little work has been done in deep RL to handle partially observable environments. We propose a new architecture called Action-specific Deep Recurrent Q-Network (ADRQN) to enhance learning performance in partially observable domains. Actions are encoded by a fully connected layer and coupled with a convolutional observation to form an action-observation pair. The time series of action-observation pairs are then integrated by an LSTM layer that learns latent states based on which a fully connected layer computes Q-values as in conventional Deep Q-Networks (DQNs). We demonstrate the effectiveness of our new architecture in several partially observable domains, including flickering Atari games.

Title: Semiclassical "Divide-and-Conquer" Method for Spectroscopic Calculations of High Dimensional Molecular Systems, Abstract: A new semiclassical "divide-and-conquer" method is presented with the aim of demonstrating that quantum dynamics simulations of high dimensional molecular systems are doable. The method is first tested by calculating the quantum vibrational power spectra of water, methane, and benzene - three molecules of increasing dimensionality for which benchmark quantum results are available - and then applied to C60, a system characterized by 174 vibrational degrees of freedom. Results show that the approach can accurately account for quantum anharmonicities, purely quantum features like overtones, and the removal of degeneracy when the molecular symmetry is broken.

Title: Yangian Symmetry and Integrability of Planar N=4 Super-Yang-Mills Theory, Abstract: In this letter we establish Yangian symmetry of planar N=4 super-Yang-Mills theory. We prove that the classical equations of motion of the model close onto themselves under the action of Yangian generators. Moreover we propose an off-shell extension of our statement which is equivalent to the invariance of the action and prove that it is exactly satisfied. We assert that our relationship serves as a criterion for integrability in planar gauge theories by explicitly checking that it applies to integrable ABJM theory but not to non-integrable N=1 super-Yang-Mills theory.

Title: Allocation strategies for high fidelity models in the multifidelity regime, Abstract: We propose a novel approach to allocating resources for expensive simulations of high fidelity models when used in a multifidelity framework. Allocation decisions that distribute computational resources across several simulation models become extremely important in situations where only a small number of expensive high fidelity simulations can be run. We identify this allocation decision as a problem in optimal subset selection, and subsequently regularize this problem so that solutions can be computed. Our regularized formulation yields a type of group lasso problem that has been studied in the literature to accomplish subset selection. Our numerical results compare performance of algorithms that solve the group lasso problem for algorithmic allocation against a variety of other strategies, including those based on classical linear algebraic pivoting routines and those derived from more modern machine learning-based methods. We demonstrate on well known synthetic problems and more difficult real-world simulations that this group lasso solution to the relaxed optimal subset selection problem performs better than the alternatives.

Title: Setting the threshold for high throughput detectors: A mathematical approach for ensembles of dynamic, heterogeneous, probabilistic anomaly detectors, Abstract: Anomaly detection (AD) has garnered ample attention in security research, as such algorithms complement existing signature-based methods but promise detection of never-before-seen attacks. Cyber operations manage a high volume of heterogeneous log data; hence, AD in such operations involves multiple (e.g., per IP, per data type) ensembles of detectors modeling heterogeneous characteristics (e.g., rate, size, type) often with adaptive online models producing alerts in near real time. Because of high data volume, setting the threshold for each detector in such a system is an essential yet underdeveloped configuration issue that, if slightly mistuned, can leave the system useless, either producing a myriad of alerts and flooding downstream systems, or giving none. In this work, we build on the foundations of Ferragut et al. to provide a set of rigorous results for understanding the relationship between threshold values and alert quantities, and we propose an algorithm for setting the threshold in practice. Specifically, we give an algorithm for setting the threshold of multiple, heterogeneous, possibly dynamic detectors completely a priori, in principle. Indeed, if the underlying distribution of the incoming data is known (closely estimated), the algorithm provides provably manageable thresholds. If the distribution is unknown (e.g., has changed over time) our analysis reveals how the model distribution differs from the actual distribution, indicating a period of model refitting is necessary. We provide empirical experiments showing the efficacy of the capability by regulating the alert rate of a system with $\approx$2,500 adaptive detectors scoring over 1.5M events in 5 hours. Further, we demonstrate on the real network data and detection framework of Harshaw et al. the alternative case, showing how the inability to regulate alerts indicates the detection model is a bad fit to the data.

Title: Assessment of algorithms for computing moist available potential energy, Abstract: Atmospheric moist available potential energy (MAPE) has been traditionally defined as the potential energy of a moist atmosphere relative to that of the adiabatically sorted reference state defining a global potential energy minimum. Finding such a reference state was recently shown to be a linear assignment problem, and therefore exactly solvable. However, this is computationally extremely expensive, so there has been much interest in developing heuristic methods for computing MAPE in practice. Comparisons of the accuracy of such approximate algorithms have so far been limited to a small number of test cases; this work provides an assessment of the algorithms' performance across a wide range of atmospheric soundings, in two different locations. We determine that the divide-and-conquer algorithm is the best suited to practical application, but suffers from the previously overlooked shortcoming that it can produce a reference state with higher potential energy than the actual state, resulting in a negative value of MAPE. Additionally, we show that it is possible to construct an algorithm exploiting a theoretical expression linking MAPE to Convective Available Potential Energy (CAPE) previously derived by Kerry Emanuel. This approach has a similar accuracy to existing approximate sorting algorithms, whilst providing greater insight into the physical source of MAPE. In light of these results, we discuss how to make progress towards constructing a satisfactory moist APE theory for the atmosphere. We also outline a method for vectorising the adiabatic lifting of moist air parcels, which increases the computational efficiency of algorithms for calculating MAPE, and could be used for other applications such as convection schemes.

Title: Don't Panic! Better, Fewer, Syntax Errors for LR Parsers, Abstract: Syntax errors are generally easy to fix for humans, but not for parsers, in general, and LR parsers, in particular. Traditional 'panic mode' error recovery, though easy to implement and applicable to any grammar, often leads to a cascading chain of errors that drown out the original. More advanced error recovery techniques suffer less from this problem but have seen little practical use because their typical performance was seen as poor, their worst case unbounded, and the repairs they reported arbitrary. In this paper we show two generic error recovery algorithms that fix all three problems. First, our algorithms are the first to report the complete set of possible repair sequences for a given location, allowing programmers to select the one that best fits their intention. Second, on a corpus of 200,000 real-world syntactically invalid Java programs, we show that our best performing algorithm is able to repair 98.71% of files within a cut-off of 0.5s. Furthermore, we are also able to use the complete set of repair sequences to reduce the cascading error problem even further than previous approaches. Our best performing algorithm reports 442,252.0 error locations in the corpus to the user, while the panic mode algorithm reports 980,848.0 error locations: in other words, our algorithms reduce the cascading error problem by well over half.

Title: Deep Energy Estimator Networks, Abstract: Density estimation is a fundamental problem in statistical learning. This problem is especially challenging for complex high-dimensional data due to the curse of dimensionality. A promising solution to this problem is given here in an inference-free hierarchical framework that is built on score matching. We revisit the Bayesian interpretation of the score function and the Parzen score matching, and construct a multilayer perceptron with a scalable objective for learning the energy (i.e. the unnormalized log-density), which is then optimized with stochastic gradient descent. In addition, the resulting deep energy estimator network (DEEN) is designed as products of experts. We present the utility of DEEN in learning the energy, the score function, and in single-step denoising experiments for synthetic and high-dimensional data. We also diagnose stability problems in the direct estimation of the score function that had been observed for denoising autoencoders.

Title: Cross-Entropy Loss and Low-Rank Features Have Responsibility for Adversarial Examples, Abstract: State-of-the-art neural networks are vulnerable to adversarial examples; they can easily misclassify inputs that are imperceptibly different than their training and test data. In this work, we establish that the use of cross-entropy loss function and the low-rank features of the training data have responsibility for the existence of these inputs. Based on this observation, we suggest that addressing adversarial examples requires rethinking the use of cross-entropy loss function and looking for an alternative that is more suited for minimization with low-rank features. In this direction, we present a training scheme called differential training, which uses a loss function defined on the differences between the features of points from opposite classes. We show that differential training can ensure a large margin between the decision boundary of the neural network and the points in the training dataset. This larger margin increases the amount of perturbation needed to flip the prediction of the classifier and makes it harder to find an adversarial example with small perturbations. We test differential training on a binary classification task with CIFAR-10 dataset and demonstrate that it radically reduces the ratio of images for which an adversarial example could be found -- not only in the training dataset, but in the test dataset as well.

Title: Parametrizing modified gravity for cosmological surveys, Abstract: One of the challenges in testing gravity with cosmology is the vast freedom opened when extending General Relativity. For linear perturbations, one solution consists in using the Effective Field Theory of Dark Energy (EFT of DE). Even then, the theory space is described in terms of a handful of free functions of time. This needs to be reduced to a finite number of parameters to be practical for cosmological surveys. We explore in this article how well simple parametrizations, with a small number of parameters, can fit observables computed from complex theories. Imposing the stability of linear perturbations appreciably reduces the theory space we explore. We find that observables are not extremely sensitive to short time-scale variations and that simple, smooth parametrizations are usually sufficient to describe this theory space. Using the Bayesian Information Criterion, we find that using two parameters for each function (an amplitude and a power law index) is preferred over complex models for 86% of our theory space.

Title: Counterfactuals, indicative conditionals, and negation under uncertainty: Are there cross-cultural differences?, Abstract: In this paper we study selected argument forms involving counterfactuals and indicative conditionals under uncertainty. We selected argument forms to explore whether people with an Eastern cultural background reason differently about conditionals compared to Westerners, because of the differences in the location of negations. In a 2x2 between-participants design, 63 Japanese university students were allocated to four groups, crossing indicative conditionals and counterfactuals, and each presented in two random task orders. The data show close agreement between the responses of Easterners and Westerners. The modal responses provide strong support for the hypothesis that conditional probability is the best predictor for counterfactuals and indicative conditionals. Finally, the grand majority of the responses are probabilistically coherent, which endorses the psychological plausibility of choosing coherence-based probability logic as a rationality framework for psychological reasoning research.

Title: Two Posets of Noncrossing Partitions Coming From Undesired Parking Spaces, Abstract: Consider the noncrossing set partitions of an $n$-element set which either do not contain the block $\{n-1,n\}$, or which do not contain the singleton block $\{n\}$ whenever $1$ and $n-1$ are in the same block. In this article we study the subposet of the noncrossing partition lattice induced by these elements, and show that it is a supersolvable lattice, and therefore lexicographically shellable. We give a combinatorial model for the NBB bases of this lattice and derive an explicit formula for the value of its Möbius function between least and greatest element. This work is motivated by a recent article by M. Bruce, M. Dougherty, M. Hlavacek, R. Kudo, and I. Nicolas, in which they introduce a subposet of the noncrossing partition lattice that is determined by parking functions with certain forbidden entries. In particular, they conjecture that the resulting poset always has a contractible order complex. We prove this conjecture by embedding their poset into ours, and showing that it inherits the lexicographic shellability.

Title: Local White Matter Architecture Defines Functional Brain Dynamics, Abstract: Large bundles of myelinated axons, called white matter, anatomically connect disparate brain regions together and compose the structural core of the human connectome. We recently proposed a method of measuring the local integrity along the length of each white matter fascicle, termed the local connectome. If communication efficiency is fundamentally constrained by the integrity along the entire length of a white matter bundle, then variability in the functional dynamics of brain networks should be associated with variability in the local connectome. We test this prediction using two statistical approaches that are capable of handling the high dimensionality of data. First, by performing statistical inference on distance-based correlations, we show that similarity in the local connectome between individuals is significantly correlated with similarity in their patterns of functional connectivity. Second, by employing variable selection using sparse canonical correlation analysis and cross-validation, we show that segments of the local connectome are predictive of certain patterns of functional brain dynamics. These results are consistent with the hypothesis that structural variability along axon bundles constrains communication between disparate brain regions.

Title: Audio to Body Dynamics, Abstract: We present a method that gets as input an audio of violin or piano playing, and outputs a video of skeleton predictions which are further used to animate an avatar. The key idea is to create an animation of an avatar that moves their hands similarly to how a pianist or violinist would do, just from audio. Aiming for a fully detailed correct arms and fingers motion is a goal, however, it's not clear if body movement can be predicted from music at all. In this paper, we present the first result that shows that natural body dynamics can be predicted at all. We built an LSTM network that is trained on violin and piano recital videos uploaded to the Internet. The predicted points are applied onto a rigged avatar to create the animation.

Title: Detecting causal associations in large nonlinear time series datasets, Abstract: Identifying causal relationships from observational time series data is a key problem in disciplines such as climate science or neuroscience, where experiments are often not possible. Data-driven causal inference is challenging since datasets are often high-dimensional and nonlinear with limited sample sizes. Here we introduce a novel method that flexibly combines linear or nonlinear conditional independence tests with a causal discovery algorithm that allows to reconstruct causal networks from large-scale time series datasets. We validate the method on a well-established climatic teleconnection connecting the tropical Pacific with extra-tropical temperatures and using large-scale synthetic datasets mimicking the typical properties of real data. The experiments demonstrate that our method outperforms alternative techniques in detection power from small to large-scale datasets and opens up entirely new possibilities to discover causal networks from time series across a range of research fields.

Title: Rational approximations to the zeta function, Abstract: This article describes a sequence of rational functions which converges locally uniformly to the zeta function. The numerators (and denominators) of these rational functions can be expressed as characteristic polynomials of matrices that are on the face of it very simple. As a consequence, the Riemann hypothesis can be restated as what looks like a rather conventional spectral problem but which is related to the one found by Connes in his analysis of the zeta function. However the point here is that the rational approximations look to be susceptible of quantitative estimation.

Title: The COS-Halos Survey: Metallicities in the Low-Redshift Circumgalactic Medium, Abstract: We analyze new far-ultraviolet spectra of 13 quasars from the z~0.2 COS-Halos survey that cover the HI Lyman limit of 14 circumgalactic medium (CGM) systems. These data yield precise estimates or more constraining limits than previous COS-Halos measurements on the HI column densities NHI. We then apply a Monte-Carlo Markov Chain approach on 32 systems from COS-Halos to estimate the metallicity of the cool (T~10^4K) CGM gas that gives rise to low-ionization state metal lines, under the assumption of photoionization equilibrium with the extragalactic UV background. The principle results are: (1) the CGM of field L* galaxies exhibits a declining HI surface density with impact parameter Rperp (at >99.5%$ confidence), (2) the transmission of ionizing radiation through CGM gas alone is 70+/-7%; (3) the metallicity distribution function of the cool CGM is unimodal with a median of 1/3 Z_Sun and a 95% interval from ~1/50 Z_Sun to over 3x solar. The incidence of metal poor (<1/100 Z_Sun) gas is low, implying any such gas discovered along quasar sightlines is typically unrelated to L* galaxies; (4) we find an unexpected increase in gas metallicity with declining NHI (at >99.9% confidence) and, therefore, also with increasing Rperp. The high metallicity at large radii implies early enrichment; (5) A non-parametric estimate of the cool CGM gas mass is M_CGM_cool = 9.2 +/- 4.3 10^10 Msun, which together with new mass estimates for the hot CGM may resolve the galactic missing baryons problem. Future analyses of halo gas should focus on the underlying astrophysics governing the CGM, rather than processes that simply expel the medium from the halo.

Title: Towards Bursting Filter Bubble via Contextual Risks and Uncertainties, Abstract: A rising topic in computational journalism is how to enhance the diversity in news served to subscribers to foster exploration behavior in news reading. Despite the success of preference learning in personalized news recommendation, their over-exploitation causes filter bubble that isolates readers from opposing viewpoints and hurts long-term user experiences with lack of serendipity. Since news providers can recommend neither opposite nor diversified opinions if unpopularity of these articles is surely predicted, they can only bet on the articles whose forecasts of click-through rate involve high variability (risks) or high estimation errors (uncertainties). We propose a novel Bayesian model of uncertainty-aware scoring and ranking for news articles. The Bayesian binary classifier models probability of success (defined as a news click) as a Beta-distributed random variable conditional on a vector of the context (user features, article features, and other contextual features). The posterior of the contextual coefficients can be computed efficiently using a low-rank version of Laplace's method via thin Singular Value Decomposition. Efficiencies in personalized targeting of exceptional articles, which are chosen by each subscriber in test period, are evaluated on real-world news datasets. The proposed estimator slightly outperformed existing training and scoring algorithms, in terms of efficiency in identifying successful outliers.

Title: Nonlinear Loewy Factorizable Algebraic ODEs and Hayman's Conjecture, Abstract: In this paper, we introduce certain $n$-th order nonlinear Loewy factorizable algebraic ordinary differential equations for the first time and study the growth of their meromorphic solutions in terms of the Nevanlinna characteristic function. It is shown that for generic cases all their meromorphic solutions are elliptic functions or their degenerations and hence their order of growth are at most two. Moreover, for the second order factorizable algebraic ODEs, all the meromorphic solutions of them (except for one case) are found explicitly. This allows us to show that a conjecture proposed by Hayman in 1996 holds for these second order ODEs.

Title: An Event-based Fast Movement Detection Algorithm for a Positioning Robot Using POWERLINK Communication, Abstract: This work develops a tracking system based on an event-based camera. A bioinspired filtering algorithm to reduce noise and transmitted data while keeping the main features at the scene is implemented in FPGA which also serves as a network node. POWERLINK IEEE 61158 industrial network is used to communicate the FPGA with a controller connected to a self-developed two axis servo-controlled robot. The FPGA includes the network protocol to integrate the event-based camera as any other existing network node. The inverse kinematics for the robot is included in the controller. In addition, another network node is used to control pneumatic valves blowing the ball at different speed and trajectories. To complete the system and provide a comparison, a traditional frame-based camera is also connected to the controller. The imaging data for the tracking system are obtained either from the event-based or frame-based camera. Results show that the robot can accurately follow the ball using fast image recognition, with the intrinsic advantages of the event-based system (size, price, power). This works shows how the development of new equipment and algorithms can be efficiently integrated in an industrial system, merging commercial industrial equipment with the new devices so that new technologies can rapidly enter into the industrial field.